Submicron Emitters Enable Reliable Quantification of Weak Protein–Glycan Interactions by ESI-MS
Bibliographic record
Abstract
Interactions between carbohydrates (glycans) and glycan-binding proteins (GBPs) regulate a wide variety of important biological processes. However, the affinities of most monovalent glycan–GBP complexes are typically weak (dissociation constant ( K d ) > μM) and difficult to reliably measure with conventional assays; consequently, the glycan specificities of most GBPs are not well established. Here, we demonstrate how electrospray ionization mass spectrometry (ESI-MS), implemented with nanoflow ESI emitters with inner diameters of ∼50 nm, allows for the facile quantification of low-affinity glycan–GBP interactions. The small size of the droplets produced from these submicron emitters effectively eliminates the formation of nonspecific glycan–GBP binding (false positives) during the ESI process up to ∼mM glycan concentrations. Thus, interactions with affinities as low as ∼5 mM can be measured directly from the mass spectrum. The general suppression of nonspecific adducts (including nonvolatile buffers and salts) achieved with these tips enables ESI-MS glycan affinity measurements to be performed on C-type lectins, a class of GBPs that bind glycans in a calcium-dependent manner and are important regulators of immune response. At physiologically relevant calcium ion concentrations (2–3 mM), the extent of Ca 2+ nonspecific adduct formation observed using the submicron emitters is dramatically suppressed, allowing glycan affinities, and the influence of Ca 2+ thereon, to be measured. Finally, we show how the use of submicron emitters and suppression of nonspecific binding enable the quantification of labile (prone to in-source dissociation) glycan–GBP interactions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".